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Training data influence shifts dramatically over the course of language model pretraining, with literature data dominating early and STEM data taking over later.
The first-ever SCPI classifier for Japanese text reveals significant challenges in detecting sensitive information, crucial for compliance with privacy regulations.
Open-weight models lag significantly behind proprietary counterparts on a new Japanese VQA benchmark, highlighting critical gaps in chart and table understanding.
Achieve 3x faster video captioning without sacrificing accuracy by swapping quadratic attention for a linear Mamba backbone and hierarchical bidirectional scanning.
Training VLMs on Jagle, the largest Japanese multimodal dataset, not only crushes existing models on Japanese tasks, but *also* boosts English performance when combined with English data.
Japanese VQA benchmarks are riddled with issues that lead to misleading model comparisons, but JAMMEval fixes this with a rigorous, two-stage refinement process.
Optimizing multilingual training? Shapley values reveal the hidden cross-lingual transfer effects that current scaling laws miss, leading to better language mixture ratios.
